Influence of high molecular weight polypeptides on the mouthfeel of commercial beer

Low malt beers have high sales volumes in Japan, but improving their mouthfeel, including softness, smoothness and decreasing astringency, is challenging because the compounds responsible remain unclear. In this study, beer was fractionated by preparative size‐exclusion chromatography, with the polypeptide and maltodextrin fractions purified using solid‐phase extraction and ion‐exchange resin. Sensory data from a spike test showed that the mouthfeel (softness, smoothness, and reduced astringency) of low malt beer was improved both by the degree of polymerisation (DP) of maltodextrins (DP of 2‐10; at increased concentration of 40 to 60%; P < 0.01) and by 10 ‐ 20 kilodalton (kDa) high molecular weight (HMW) polypeptide and 2‐3 kDa low molecular weight polypeptide fractions (at a 50% increase in concentration; P < 0.01). Furthermore, highly purified 10 to 20 kDa HMW polypeptides improved the softness and smoothness and decreased the astringency (at a 25% increase in concentration). This report is the first to provide experimental sensory data indicating that HMW polypeptides improve the mouthfeel of beer. Based on these findings, a new low malt beer was developed that showed significantly higher levels of the 10‐20 kDa HMW polypeptides with an overall improved mouthfeel. Mass spectrometric analysis of the 10 to 20 kDa proteins identified several unique foam positive proteins, including barley dimeric alpha‐amylase inhibitor‐1 and non‐specific lipid‐transfer protein 1. These 10‐20 kDa HMW proteins are likely to be responsible for the improved mouthfeel of beer. © 2020 Kirin Holdings Kabushik Kaisha Co. Ltd. Journal of the Institute of Brewing published by John Wiley & Sons Ltd on behalf of The Institute of Brewing & Distilling     

A state-space modeling approach for localization of focal current sources from MEG

State-space modeling is a promising approach for current source reconstruction from magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of inverse solutions in a flexible manner. However, state-space model-based source localization research remains underdeveloped; extraction of spatially focal current sources and handling of the high dimensionality of the distributed source model remain problematic. In this study, we propose a novel state-space model-based method that resolves these problems, extending our previous source localization method to include a temporal constraint by state-space modeling. To enable focal current reconstruction, we account for spatially inhomogeneous temporal dynamics by introducing dynamics model parameters that differ for each cortical position. The model parameters and the intensity of the current sources are jointly estimated according to a bayesian framework. We circumvent the high dimensionality of the problem by assuming prior distributions of the model parameters to reduce the sensitivity to unmodeled components, and by adopting variational bayesian inference to reduce the computational cost. Through simulation experiments and application to real MEG data, we have confirmed that our proposed method successfully reconstructs focal current activities, which evolve with their temporal dynamics.     

Feature extraction of protein expression levels based on classification of functional foods with SOM

We investigated the relations between the physiological activities and protein expression levels of functional foods using a self-organizing map (SOM). The input vectors to the SOM were the protein expression levels and the physiological activity. A competitive node has two kinds of weights: one is for protein expression levels, and the other is for physiological activity. A winner node is decided by the distance between the values of protein expression levels described in the input vector and the corresponding weights only. Then all weights, including those for physiological activity in each node, are updated. Therefore each node has an artificially generated value of physiological activity. Finally, the nodes can be categorized by the abovementioned physiological activity. A well-trained SOM gives us information about the relations between physiological activities and protein expression levels. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Properties and estimation of mullite thermal insulators prepared by gelation–freezing with alumina nanofibers

Thermal insulators were fabricated by freezing gelatin gels containing calcined kaolinite with alumina nanofibers, followed by sintering. The resultant porosity could be varied from 81.0% to 89.8% by solid loadings in the initial slurry. The relationship among porosity, microstructure, compressive strength, and thermal conductivity was examined. The compressive strength and thermal conductivities of the insulators prepared with initial solid loadings from 3 to 6 vol% ranged from 11.6 to 56.7 MPa and from .25 to .46 W/mK, respectively. Those properties were also estimated by simulation using modeling of overall pore morphology, resulting in good agreement.     

Recent trends of advanced ceramics industry and Fine Ceramics Roadmap 2050

Japan's fine ceramics (advanced ceramics) industry, which reached US$30 billion of production in 2018 (an annual growth rate of 6.3%), accounts for 40% of the global market. The mission of the Japan Fine Ceramics Association (JFCA), which consists of 116 corporate members related to fine ceramics, is to further promote the development of this industry. In this paper, we describe the recent trends of fine ceramics industry and the latest JFCA's activities, including the publication of the FC Roadmap 2050 (2021 edition). This roadmap addresses fine ceramics technologies and products that will meet increasingly diverse needs of society, market, and industry in the year 2050.     

Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network

The fracture toughness of silicon nitride (Si₃N₄) ceramics was evaluated directly from their microstructures via deep learning using convolutional neural network models. Totally 156 data sets containing microstructural images and relative densities were prepared from 45 types of Si₃N₄ samples as input feature quantities (IFQs) and were correlated to the fracture toughness as an objective variable. The data sets were divided into two groups. One was used for training, resulting in the creation of regression models for two kinds of IFQs: the microstructures only and a combination of the microstructures and the relative densities. The other group was used for testing the validity of the created models. As a result, the determination coefficient was approximately 0.8 even when using only the microstructures as the IFQs and was further improved when adding the relative densities. It was revealed that the fracture toughness of Si₃N₄ ceramics was well evaluated from their microstructures.     

Effects of nitridation temperature on properties of sintered reaction-bonded silicon nitride

We prepared sintered reaction-bonded silicon nitride ceramics by using yttria and magnesia as sintering additives and evaluated effects of the nitridation temperature on their microstructure, bending strength, fracture toughness, and thermal conductivity. The effects of the nitridation temperature were large, but different depending on the property. The ratio of β-phase in the nitrided compacts significantly increased with increasing the nitridation temperature, whereas their microstructures had no clear difference. Although the bending strength varied, it maintains a high value of 800 MPa. Fracture toughness was almost constant regardless the temperature. The thermal conductivity improved as the β-phase in the nitrided compact increases. This resulted in a decrease of the lattice oxygen content and increase of the thermal conductivity. Therefore, elevating the nitridation temperature and consequently the β-phase ratio should be a promising strategy for achieving compatibly high strength and high thermal conductivity, which are generally known to be in a trade-off relationship.     

Effects of nitrogen pressure on properties of sintered reaction-bonded silicon nitride

We prepared sintered reaction-bonded silicon nitride ceramics by using yttria and magnesia as sintering additives and evaluated the effects of nitrogen pressure (0.1–1.0 MPa) on their microstructure, bending strength, fracture toughness, and thermal conductivity. The ratio of β phase in the nitrided compacts varied with the pressure and increased with increasing it. Since many β grains in the nitrided compacts were formed and interlocked each other with a stable three-dimensional structure which restricted the shrinkage during the sintering procedure, many pores remained in the sintered body. Under the middle pressure (0.3–0.5 MPa), the grains grew large because the number of formed nuclei was small. On the other hand, under the high pressures (0.8–1.0 MPa), the grains were relatively fine and uniform because of a large number of nuclei. Since the porosity and grain length depended on the nitridation mechanism, which was affected by the nitrogen pressure, the properties largely varied accordingly. The nitridation at 0.1 MPa gave the best properties in this study.